Big data, little decisions – some articles

Big Data is a big topic these days. The combination of predictive analytics and Big Data should create new opportunities for more specific, future-oriented analysis of large amounts of data to create actionable insight. Yet most organizations are not combining predictive analytics and Big Data in this way and I think this is because they are trying to apply these technologies to the wrong problem. When large investments must be made, as for Big Data, organizations worry about showing a return on this investment. As a result they focus on the big problems that they have, figuring these will offer the best return. When it comes to Big Data and predictive analytics, however, they would do better to focus on operational day to day decisions.

Using Decision Management to focus predictive analytics on these “little decisions” offers tremendous potential because these are the decisions that are best suited to improvement using predictive analytics and the cumulative value of these decisions is often far greater than the value of one–off strategic decisions.

Because these day to day decisions happen often they create lots of data about what works and what does not, perfect for subsequent analysis.

Because these decisions drive operational processes and responses they are closely tied to your performance metrics, making it easier to see what good decisions look like (good decisions move your KPIs in a positive direction)

Because these decisions happen so often they multiple any improvement in results many times – a small improvement in a decision made thousands or millions of times a year makes for a big result

Finally these decisions are often about consumers and Big Data is often about consumers and their behavior